Are These The 7 Real Reasons Why Tech Projects Fail?

According to reports, 25 percent of technology projects fail outright; 20 to 25 percent don’t show any return on investment; and as much as 50 percent need massive reworking by the time they’re finished.

But the question is why: Why do so many technology projects fail — and fail so miserably? From my experience, it’s usually not tech problems that derail tech projects. In fact, 54 percent of IT project failures can be attributed to poor management — while only 3 percent are due to technological problems.

Poorly defined (or no defined) outcome.Believe it or not, one of the more common problems I see with both technology projects and data projects is an ill defined goal or outcome. A company will say they want to improve customer service, for example — but no one ever bothers to say what that looks like. Shorter call times? Fewer calls? Higher customer satisfaction? How will you know when you’ve succeeded? If you don’t know, you’re doomed to failure.

Lack of leadership.Too often, technology projects are deemed “IT” projects and relegated to the IT department, regardless of what the project actually is. But for any project to work, it needs strong leadership from the top down. If a project doesn’t have buy in and support from C-level executives as well as specific department leaders, it’s hard to get employees on board and hard to know who is in charge when leadership questions arise.

Lack of accountability.When projects are dubbed “IT projects” and left to the IT department, there’s also a lack of accountability that can develop. Executives may wrongly believe that they can’t understand what’s happening, and leave it to the tech guys to figure out. This is a mistake. If your tech team can’t adequately explain what’s happening on the project or why it’s needed, that’s a huge red flag. And if the executives aren’t driving the project and holding the team accountable, it can easily spiral out of control.

Insufficient communication.As I mentioned above, someone on the tech team needs to be able to explain the project details regularly to the “non-tech” executives and other involved parties. It’s vital for someone on the team to have strong visualization and storytelling skills in order to communicate clearly and regularly what’s happening with the project.

No plan or timeline.Without a clear timeline and plan with progress markers, any project (but technology projects in particular) can wander off the original path and meander through many detours and cul du sacs. A clear plan and someone to keep track of it is vital for keeping these projects moving forward.

Lack of user testing, or failure to address feedback.The thing about technology projects is that ultimately, they’re made for people, not machines. A lack of real-world user testing before launch is a common problem. The data scientists, programmers and engineers think they know what users want, but users may have an entirely different set of needs and problems. Once user testing is conducted, the project has to prioritize addressing the feedback, or the end user won’t be happy — and ultimately won’t use the technology created for them.

Solving the wrong problem.I’ve seen this time and time again with big data projects: companies think they’re creating something to address the problem, but it turns out they’re addressing the wrong problem. In our customer service example, if the company decides that shorter call times is the metric for improved customer service, employees become incentivized to get off the phone as quickly as possible, which may or may not actually improve customer service. Yes, call time decreases, but customers may be even less satisfied than before.

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With any project — technology or otherwise — you must answer a few key questions first:

Before embarking on any IT project (including big data projects), ask and answer these five essential questions:

What business problem do I need to solve?

Start with the strategy and big business challenges or questions first, and then decide:

What data or tools will I need to solve the problem?

Try to keep this as simple as possible to avoid scope creep. What’s the simplest possible answer to your problem?

How will I measure success?

This is where you must set key performance indicators (KPI) to be monitored and ensure that the metrics you choose actually measure the problem you hope to solve.

How will I present the insights?

All the fancy data sets and cool analytics don’t mean anything if they aren’t presented to the right people in the right way in order to facilitate decision making. Keeping your target audience in mind is perhaps the most important thing to remember.

How will I test and implement the solution?

Put a plan in place from the start to involve end users in the testing and ensure that their feedback gets addressed before the final phase of the project